Machine Learning Engineer

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Description

In this role you will work in the Platform team – a function for the deployment and evolution of the backend platform that underpins the core of the Xantura business. Own and advance a predictive modelling platform that scales across problem types and tenants, using it to design, implement, and iterate models (embedding-based sequence encoders, temporal survival models, gradient-boosted decision trees) that predict key vulnerabilities in housing, health, and other social domains. Track developments in ML and frontier models, running structured experiments to bring promising techniques into production safely. Build robust evaluation pipelines, training datasets, and model infrastructure to support continuous improvement of natural language & predictive analytics. Ensure responsible AI deployment, embedding ethical and regulatory considerations into every stage of development. Bachelor’s or Master’s degree in Computer Science, Machine Learning, or a related technical field – or equivalent practical experience. 3+ years of professional experience as an ML Engineer, or related role. Strong programming skills and production experience in Python. ). g. PyTorch, scikit-learn, and gradient-boosting libraries such as XGBoost or LightGBM. g. via FastAPI; Deploying containerised systems to production, in particular via Kubernetes. In addition, the following would be an advantage: PhD in Computer Science, Machine Learning, or a related field with a strong publication record in text analytics, representation learning, or applied predictive modelling. g. g. via LangChain, AutoGen, PydanticAI). Evidence of participating in Open-Source Software (OSS) development, public hackathons, or other sharable coding samples. Deep expertise in embedding-based architectures, including bi-encoders, cross-encoders, etc. for long-horizon text or temporal prediction tasks. Practical experience building and serving production-ready, asynchronous APIs for embedding and/or other compute-intensive services. Proficiency in Python for building high-performance data and model pipelines, with strong software engineering discipline (testing, versioning, CI/CD). Good familiarity with the Azure ecosystem (Azure Kubernetes Service, Azure Batch, Azure AI Foundry, Azure Machine Learning, Azure Blob Storage, Azure Key Vault) . This is a Hybrid opportunity with the expectations of being in the office 1 - 2 days a week.

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